Iterative image formation using fast (Re/Back)-projection for spotlight-mode SAR

Abstract

Iterative SAR image formation can visually improve image reconstructions from under-sampled phase histories by approximately solving a regularised least squares problem. For iterative inversion to be computationally feasible, fast algorithms for the observation matrix and its adjoint must be available. We demonstrate how fast, N 2 log 2 N complexity, (re/back)-projection algorithms can be used as accurate approximations for the observation matrix and its adjoint, without the limiting assumptions of other N 2 log 2 N methods, e.g. the polar format algorithm. Experimental results demonstrate the effectiveness of iterative methods using a publicly available SAR dataset. Matlab/C code implementations of the fast (re/back)-projection algorithms used in this paper have been made available.

A study of material identification using SAR

“…Phase history generation for each material is accomplished using reprojection, which computes the phase history using a combination of one-dimensional FFTs and interpolation operations that is essentially the reverse process of backprojection. See, e.g., [8] for a discussion of this approach along with techniques for numerical acceleration. Uniformly distributed random phases are added to each simulated point scatterer to avoid artifacts from the underlying simulation grid.…”

Auto-focus for under-sampled synthetic aperture radar

“…The approaches used in [9] and [10] are of this form. This type of method is known to be stable assuming we can solve C, i.e.…”

“…To realistically model phase errors we add errors to the supplied aperture position data such that errors in the distance to the scene centre measurements, which are used in observational model, are normally distributed with a variance of 6.8 × 10 −6 meters. In this experiment, the more general -non Fourier-model (4) is used, where we compute the observation model and its adjoint using the fast (re/back)-projection algorithms from [10]. A classical auto-focus method was not used in this experiment because even with only two degrees of the Gotcha data set, the observation model is not well-approximated by (4).…”

Automatic target recognition from highly incomplete SAR data

“…Thus, only the sparse part is required to be recovered, and the background estimation is out of the scope of this paper. Interested readers may refer to our previous contributions 4,9,18,20 for approaches leading to improved background reconstruction.…”